# Summary (At Least 5 values)

Introduction

As a result of smartphones slowly becoming a crucial part of modern day society, the demand for mobile applications has increased greatly. The number of apps on the App Stores has been expanding at an increased rate due to app developers constantly publishing apps to meet demand. According to Apple and Google, in 2019, there are 2.2 million and 2.8 million apps available for download on their respective App Stores. We have downloaded two different datasets “Mobile App Store” (collected July 2017) and “Google Play Store Apps”(April 2019). These two data sets contain information regarding apps on the Apple App store and Google Play store such as price, rating, genre, size, and content rating. People that own a smartphone device have their own unique conglomeration of different types of apps based on their interests and personality, making it more important to find trends in mobile apps to aid in the creation of successful apps. We will be analyzing and comparing the previously mentioned data sets to determine what makes an app successful on the different app stores. This data will create useful mobile app analytics to help developers understand existing strategies and categories that drive growth and retention of future app users.

Summary (At Least 5 values)

For both app stores we have aggregated the individual apps in each data set into their corresponding categories. Then we calculated the average rating, average price, and number of applications in those categories. We created two summary tables one for each app store, allowing us to compare and dictate which type of apps are more successful in the different app stores.

The category with the most apps created for in the Google Play Store is “Education” with a count of 32247, while for the Apple App Store it’s “Games” with a count of 3862. This tells us that these are the most heavily saturated categories in the app stores, resulting in it being more difficult for apps to gain popularity and stand out in these categories. The category with the highest average rating in the Google Play Store and Apple App Store respectively are “Books_And_Reference” with 5 and “Productivity” with 5. This tell us that more consumers are satisfied with these categories compared to others. The category with the lowest average rating in the Google Play Store and Apple App Store respectively are “Maps_And_Navigation” with “4.02” and “Catalogs” with “2.01”. This information tells us that these categories are not giving consumers as much satisfaction, which can indicate that app developers should avoid this category in order to raise the chances of developing a popular app.

Table of Aggregated Data

Google Playstore

Category avg_rating overall_avg_price paid_avg_price count
EDUCATION 4.35 0.22 5.64 32247
BOOKS_AND_REFERENCE 4.48 0.24 7.72 20816
TOOLS 4.14 0.25 4.20 20676
ENTERTAINMENT 4.26 0.11 6.41 19915
MUSIC_AND_AUDIO 4.43 0.08 4.20 17452
LIFESTYLE 4.30 0.25 11.52 14572
PERSONALIZATION 4.44 0.11 1.68 10180
FINANCE 4.04 0.15 11.53 9727
BUSINESS 4.15 0.14 10.41 9712
PRODUCTIVITY 4.17 0.25 4.37 8605
NEWS_AND_MAGAZINES 4.24 0.02 3.10 7538
HEALTH_AND_FITNESS 4.21 0.19 3.58 7143
PHOTOGRAPHY 4.17 0.12 3.40 6957
TRAVEL_AND_LOCAL 4.13 0.14 5.87 6271
SPORTS 4.29 0.84 16.90 5301
COMMUNICATION 4.24 0.12 4.23 5206
SHOPPING 4.20 0.02 3.25 5078
SOCIAL 4.36 0.04 2.71 4575
MAPS_AND_NAVIGATION 4.02 0.28 9.44 3798
MEDICAL 4.24 0.96 9.41 3571
GAME_PUZZLE 4.36 0.36 3.61 3336
FOOD_AND_DRINK 4.31 0.08 5.40 3034
VIDEO_PLAYERS 4.06 0.18 4.11 2502
GAME_CASUAL 4.24 0.20 2.91 2215
GAME_ARCADE 4.27 0.26 2.53 2154
AUTO_AND_VEHICLES 4.12 0.42 11.13 2015
GAME_EDUCATIONAL 4.26 0.40 2.91 1890
ART_AND_DESIGN 4.23 0.10 3.03 1743
GAME_SIMULATION 4.21 0.59 3.53 1515
WEATHER 4.19 0.26 3.75 1512
GAME_ACTION 4.25 0.36 2.80 1363
GAME_CARD 4.26 0.31 4.51 1039
GAME_ADVENTURE 4.32 1.34 4.07 986
GAME_ROLE_PLAYING 4.33 1.34 4.89 972
BEAUTY 4.30 0.04 12.16 939
GAME_BOARD 4.25 0.61 3.82 912
HOUSE_AND_HOME 4.04 0.04 3.14 875
GAME_WORD 4.35 0.08 2.53 858
GAME_STRATEGY 4.28 0.72 3.38 811
GAME_TRIVIA 4.21 0.26 6.70 710
GAME_SPORTS 4.16 0.49 3.83 708
GAME_RACING 4.21 0.24 2.49 686
LIBRARIES_AND_DEMO 4.22 0.13 5.08 603
EVENTS 4.33 0.00 1.24 599
PARENTING 4.34 0.13 2.94 599
COMICS 4.27 0.11 3.01 487
GAME_CASINO 4.36 0.02 2.66 332
DATING 4.05 0.08 3.99 306
GAME_MUSIC 4.11 0.27 2.24 233

This table contains information that was calculated such as the average rating per category, average price per category and how many applications per category for the Google Playstore. From this data we can see that “EDUCATION” contains the most number of applications for the specified category containing 5.64 different applications.

Apple Store

prime_genre avg_rating overall_avg_price paid_avg_price count
Games 3.69 1.43 3.45 3862
Entertainment 3.25 0.89 2.37 535
Education 3.38 4.03 5.68 453
Photo & Video 3.80 1.47 2.83 349
Utilities 3.28 1.65 2.94 248
Health & Fitness 3.70 1.92 3.32 180
Productivity 4.01 4.33 6.65 178
Social Networking 2.99 0.34 2.37 167
Lifestyle 2.81 0.89 2.55 144
Music 3.98 4.84 9.40 138
Shopping 3.54 0.02 1.99 122
Sports 2.98 0.95 3.10 114
Book 2.48 1.79 4.36 112
Finance 2.43 0.42 2.19 104
Travel 3.38 1.12 3.63 81
News 2.98 0.52 2.28 75
Weather 3.60 1.61 2.82 72
Reference 3.45 4.84 7.04 64
Food & Drink 3.18 1.55 4.89 63
Business 3.75 5.12 7.88 57
Navigation 2.68 4.12 7.30 46
Medical 3.37 8.78 13.46 23
Catalogs 2.10 0.80 7.99 10

This table contains information that was calculated such as the average rating per category, average price per category and how many applications per category for the Apple Store. From this data we can see that “Games” contains the most number of applications for the specified category containing 3.45 different applications.

Overall

After we summarized the individual app data from the data sets into a table, we have sorted the information into category, average rating, overall average price, average paid price, and count. The category column contains the available categories given to app developers by Google and Apple that sort the type of app uploaded. The average rating column calculates the average ratings given to each app category. For the average price of each category two different values were calculated. Overall Average Price is the total average price of apps in the category including free apps, which are counted as $0. While in average paid price the free apps are filtered out, so it is the average listing price of all paid apps in that category. The count column is the number of apps uploaded into each category. By using this data we are able to quickly find comparisons such as which categories have the best ratings from consumer satisfaction, which category has the highest listing price, and which category is the most app saturated. These data comparisons will help app developers determine the factors that affect the success of an app.

Visualizations

Plot 1(Amount of Applications per Category)

For this plot we created a bar chart for both Google Play Store and Apple App Store that shows the number of applications that were uploaded into each category. This chart was included to visually show which categories were more saturated with apps. For the Google Play Store it was “Education” and for the Apple App Store it was “Games”. Using these charts app developers are able to determine which genre of apps they should be developing for. Since the categories that are heavily saturated with the same types of apps will make it harder for individual apps to stand out amongst the others. The app will become lost amid the sea of similar apps making it difficult to find on the app store, ultimately impacting the number of downloads it will receive.

Plot 2 (Average Ratings V.S. Average Price)

This scatter plot is created to show the correlation between the average listing price and the average rating of apps. The listing price of apps will affect whether someone is willing to download that app, if the price is too high that will usually result in people not downloading the app. By using this plot developers are able to see how satisfied people are in comparison with the listed price to determine what price to list their apps to maximize the chances of success.

Plot 3